Research On Strata Surface Extraction Techniques From Large Scale Seismic Volume Data

As the rapid development of seismic data acquisition system and computer technology,the amount of data increase dramatically.This makes it more difficult to achieve interactive seismic data visualization and analysis on general-purpose computing platform.The idea of in-situ visualization which targets to minimize the I/O overhead and achieve the visualization and analysis of data in memory as much as possible is introduced to solve this problem.This thesis mainly studies key techniques in the real-time interactive strata visualization of large scale seismic volume data,proposes a solution to deal with it and develops a visualization software system for geophysical acquisition.The work is divided into four parts,multi-resolution volume data selection,multi-resolution iso-surface extraction,multi-resolution free surface extraction and mix display of volume and surface.For the sake of reducing the size of volume date loaded into memory,multi-resolution data is filtered according to special customized rules.On the basis of multi-resolution block data,multi-resolution strata surfaces are generated.However,there are cracks between adjacent different resolution strata surface tiles which are extracted from multi-resolution block data.Therefore a crack searching and merging algorithm is proposed to solve this problem.In crack searching section,the judgment of adjacency is implemented by special matching expressions.After attaining the cracks' positions,an adaptive crack merging algorithm is employed to generate crack-free strata surface.This interactive multi-resolution strata surface generation method can guarantee the quality of strata surface network relying on real-time acquirement and the efficiency of interaction operation at the same time.The results which have been obtained from normal PC show that the methods proposed are effective in solving problems of real-time interactive strata visualization of large scale seismic volume data on general-purpose computing platform.